Mead C. Killion and Patricia A. Niquette: “What can the pure-tone audiogram tell us about a patient's SNR loss?”, The Hearing Journal 53-3, March 2000 discloses various studies revealing that the amount of signal-to-noise ratio loss (SNR loss) for a patient with a sensorineural hearing impairment can not be accurately predicted from the audiogram. The audiogram measures (audiometric) hearing loss, the loss of sensitivity for sounds. Hearing loss can be appropriately restored by amplification of the incoming sounds. For most hearing impaired patients, the performance in speech-in-noise intelligibility tests is worse than for normal hearing people, even if the audibility of the incoming sounds is restored by amplification. The term SNR loss is defined as the average increase in signal-to-noise ratio (SNR) needed for a hearing impaired patient relative to a normal hearing person in order to achieve similar performance (50% word recognition) on a hearing in noise test, at levels above the hearing threshold. Killion found that SNR loss is relatively independent from hearing loss for most sensorineaural hearing impaired patients. Consequently, in order to determine the SNR loss for a specific patient, one needs to measure it, rather than make a guess based on the hearing loss (audiogram).
Thus, hearing impaired individuals or patients often experience at least two distinct problems: a hearing loss, which is an increase in hearing threshold level, and SNR loss, which is a loss of ability to understand high level speech in noise in comparison with normal hearing individuals.
SNR loss is traditionally estimated by measuring a speech reception threshold (SRT) of the hearing impaired individual. An individual's SRT is the signal-to-noise ratio required in a presented signal to achieve 50% correct word recognition in a hearing in noise test.
Hearing loss is typically caused by a loss of outer hair cells and conductive loss in the middle ear, while SNR loss is typically caused by a loss of inner hair cells. On average, a hearing loss of 30 to 70 dB is accompanied by a 4-7 dB SNR loss, cf. QuickSIN™ Speech in Noise Test available from Etymotic Research. However, accurate estimates of the SNR loss for a given hearing impaired individual can only be obtained by specific testing since the increase in hearing threshold level, which is measured by traditional pure-tone audiograms, and SNR loss appear to be independent characteristics.
Today's digital hearing aids that use multi-channel amplification and compression signal processing can readily restore audibility of amplified sound for a hearing impaired individual or patient. The patient's hearing ability can thus be improved by making previously inaudible speech cues audible. Loss of capability to understand speech in noise due to the above-mentioned SNR loss is accordingly the most significant problem of most hearing aid users today.
Compensating for the patient specific SNR loss has, however, proven far more difficult. While some single observation processing algorithms are able to improve an objective signal-to-noise ratio (SNR) of a noise-contaminated input signal, such as a microphone signal, a difficulty lies in the fact that filtering, i.e. attenuating or removing, noise components from the input signal introduces various artifacts into the desired signal (typical speech). These artifacts generally lead to a loss of speech cues and the single observation processing algorithms therefore fail to improve the patient's hearing ability in noisy listening environments. The most successful technique to improve the SNR of noise-contaminated speech signals has been to utilize a multi-observation system, such as a microphone array, which may contain from 2 to 5 individual microphones. An array microphone system exploits spatial differences between a desired, or target, signal and interfering noise sources. Unfortunately, many of these microphone array systems are not practical for hearing aid applications because of their accompanying requirements to surface area on a housing of the hearing prostheses. Cost and reliability issues are other factors that tend to make microphone arrays less attractive for many hearing aid applications.
Even though an ultimate goal of noise reduction systems and algorithms in hearing aids should be to improve the user's ability to hear in noise by compensating for the user's SNR loss, improving the patient's listening comfort through noise reduction is also a worthwhile achievement. In this latter situation, listening may be less tiring for the user and as such indirectly improves long-term intelligibility of noise contaminated speech signals.
As mentioned above, there exist a number of single observation and multiple observation algorithms and systems to reduce interfering noise from a target signal, e.g. speech. Since each of these algorithms and systems is associated with certain costs, there is a need for defining a strategy for selecting and applying these different noise reduction algorithms both during a fitting procedure and during normal operation of the hearing prosthesis. According to one aspect of the present invention, this problem is solved by selecting parameter values of a noise reduction algorithm or algorithms based on the patient's measured or estimated SNR loss Thereby, a degree of restoration/improvement of the SNR of noise-contaminated input signals of the hearing prosthesis has been made dependent on patient specific loss data. According to another aspect of the present invention, a hearing prosthesis capable of controlling parameters of a noise reduction algorithms in dependence on the user's current acoustic subspace, or listening environment, as recognized and indicated by the environmental classifier has been provided.